{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KNN.py\n",
    "from numpy import *\n",
    "import operator\n",
    "\n",
    "def createDataSet():\n",
    "    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])\n",
    "    labels = ['A', 'A', 'B', 'B']\n",
    "    return group, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1. ,  1.1],\n",
       "        [ 1. ,  1. ],\n",
       "        [ 0. ,  0. ],\n",
       "        [ 0. ,  0.1]]),\n",
       " ['A', 'A', 'B', 'B'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group, labels = createDataSet()\n",
    "group, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k):\n",
    "    \n",
    "    # 计算距离\n",
    "    data_set_size = dataSet.shape[0]\n",
    "    diffMat = tile(inX, (data_set_size, 1)) - dataSet\n",
    "    sqDiffMat = diffMat**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    distances = sqDistances**0.5\n",
    "    sortedDistIndices = distances.argsort()\n",
    "    \n",
    "    # 选择距离最小的k个点\n",
    "    classCount={}\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndices[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1\n",
    "    sortedClasscount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClasscount[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "group.shape: (4, 2)\n",
      "diffMat:\n",
      " [[ 4.   4.9]\n",
      " [ 4.   5. ]\n",
      " [ 5.   6. ]\n",
      " [ 5.   5.9]]\n",
      "sqDiffMat:\n",
      " [ 40.01  41.    61.    59.81]\n",
      "sortedDiffMat:\n",
      " [0 1 3 2]\n",
      "class_count:\n",
      " {'A': 1}\n"
     ]
    }
   ],
   "source": [
    "# test:\n",
    "print('group.shape:', group.shape)\n",
    "\n",
    "data_set_size = group.shape[0]\n",
    "data_set_size\n",
    "\n",
    "\n",
    "diffMat = tile(array([5,6]), (data_set_size, 1)) - group\n",
    "print('diffMat:\\n', diffMat)\n",
    "sqdiffMat = diffMat**2\n",
    "sqDistances = sqdiffMat.sum(axis=1)\n",
    "\n",
    "print('sqDiffMat:\\n', sqDistances)\n",
    "\n",
    "sortedDistIndices = sqDistances.argsort()\n",
    "\n",
    "print('sortedDiffMat:\\n', sortedDistIndices)\n",
    "\n",
    "classCount={}\n",
    "\n",
    "voteIlabel = labels[sortedDistIndices[0]]\n",
    "classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1\n",
    "\n",
    "print('class_count:\\n', classCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict\n",
    "classify0([0, 0], group, labels, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-近邻算法优化约会网站配对效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file2matrix(filename):\n",
    "    fr = open(filename)\n",
    "    arrayOLines = fr.readlines()\n",
    "    numberOfLines = len(arrayOLines)\n",
    "    returnMat = zeros((numberOfLines, 3))\n",
    "    classLabelVector = []\n",
    "    index = 0\n",
    "    for line in arrayOLines:\n",
    "        # 删除头尾的空格或者换行符\n",
    "        line = line.strip()\n",
    "        \n",
    "        listFromLine = line.split('\\t')\n",
    "        returnMat[index,:] = listFromLine[0:3]\n",
    "        classLabelVector.append(int(listFromLine[-1]))\n",
    "        index += 1\n",
    "    return returnMat, classLabelVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[  4.09200000e+04,   8.32697600e+00,   9.53952000e-01],\n",
       "        [  1.44880000e+04,   7.15346900e+00,   1.67390400e+00],\n",
       "        [  2.60520000e+04,   1.44187100e+00,   8.05124000e-01],\n",
       "        ..., \n",
       "        [  2.65750000e+04,   1.06501020e+01,   8.66627000e-01],\n",
       "        [  4.81110000e+04,   9.13452800e+00,   7.28045000e-01],\n",
       "        [  4.37570000e+04,   7.88260100e+00,   1.33244600e+00]]),\n",
       " [3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingDataMat, datingLabels = file2matrix('./dataset/datingTestSet2.txt')\n",
    "datingDataMat, datingLabels[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1889b50da90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.scatter(datingDataMat[:,1], datingDataMat[:, 2], 15.0 * array(datingLabels), 15.0 * array(datingLabels))\n",
    "# ax.scatter(datingDataMat[:,1], datingDataMat[:, 2])\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.scatter(datingDataMat[:,0], datingDataMat[:, 1], 15.0 * array(datingLabels), 15.0 * array(datingLabels))\n",
    "# ax.scatter(datingDataMat[:,1], datingDataMat[:, 2])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autoNorm(dataSet):\n",
    "    minValues = dataSet.min(0)\n",
    "    maxValues = dataSet.max(0)\n",
    "    ranges = maxValues - minValues\n",
    "    normDataSet = zeros(shape(dataSet))\n",
    "    m = dataSet.shape[0]\n",
    "    normDataSet = dataSet - tile(minValues, (m, 1))\n",
    "    normDataSet = normDataSet / tile(ranges, (m, 1))\n",
    "    return normDataSet, ranges, minValues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 0.44832535,  0.39805139,  0.56233353],\n",
       "        [ 0.15873259,  0.34195467,  0.98724416],\n",
       "        [ 0.28542943,  0.06892523,  0.47449629],\n",
       "        ..., \n",
       "        [ 0.29115949,  0.50910294,  0.51079493],\n",
       "        [ 0.52711097,  0.43665451,  0.4290048 ],\n",
       "        [ 0.47940793,  0.3768091 ,  0.78571804]]),\n",
       " array([  9.12730000e+04,   2.09193490e+01,   1.69436100e+00]),\n",
       " array([ 0.      ,  0.      ,  0.001156]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "normMat, ranges, minVals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier come back with: 3, the real answer is: 2\n",
      "the classifier come back with: 3, the real answer is: 1\n",
      "the classifier come back with: 3, the real answer is: 1\n",
      "the classifier come back with: 2, the real answer is: 3\n",
      "the classifier come back with: 3, the real answer is: 1\n",
      "error rate: 0.050000\n"
     ]
    }
   ],
   "source": [
    "def datingClassTest():\n",
    "    hoRatio = 0.1\n",
    "    datingDataMat, datingLabels = file2matrix('./dataset/datingTestSet2.txt')\n",
    "    normMat, ranges, minValues = autoNorm(datingDataMat)\n",
    "    m = normMat.shape[0]\n",
    "    numTestVecs = int(m * hoRatio)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 3)\n",
    "        if (classifierResult != datingLabels[i]):\n",
    "            print(\"the classifier come back with: %d, the real answer is: %d\"%(classifierResult, datingLabels[i]))\n",
    "            errorCount += 1.0\n",
    "    print(\"error rate: %f\"%(errorCount / float(numTestVecs)))\n",
    "\n",
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage time to play games?10\n",
      "frequent flier miles earned per year?10000\n",
      "icecream liters?0.5\n",
      "you will probably like this person:  small doses\n"
     ]
    }
   ],
   "source": [
    "def classifyPerson():\n",
    "    res_list = ['not at all', 'small doses', 'lage doses']\n",
    "    tats = float(input(\"percentage time to play games?\"))\n",
    "    miles = float(input(\"frequent flier miles earned per year?\"))\n",
    "    icecream = float(input(\"icecream liters?\"))\n",
    "    datingDataMat, datingLabels = file2matrix('./dataset/datingTestSet2.txt')\n",
    "    normMat, ranges, minValues = autoNorm(datingDataMat)\n",
    "    inArr = array([miles, tats, icecream])\n",
    "    classifierResult = classify0((inArr - minValues) / ranges, normMat, datingLabels, 3)\n",
    "    print(\"you will probably like this person: \", res_list[classifierResult -1])\n",
    "\n",
    "classifyPerson()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
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}